In:
BMC Bioinformatics, Springer Science and Business Media LLC, Vol. 20, No. S24 ( 2019-12)
Abstract:
RNA sequencing has become an increasingly affordable way to profile gene expression patterns. Here we introduce a workflow implementing several open-source softwares that can be run on a high performance computing environment. Results Developed as a tool by the Bioinformatics Shared Resource Group (BISR) at the Ohio State University, we have applied the pipeline to a few publicly available RNAseq datasets downloaded from GEO in order to demonstrate the feasibility of this workflow. Source code is available here: workflow: https://code.bmi.osumc.edu/gadepalli.3/BISR-RNAseq-ICIBM2019 and shiny: https://code.bmi.osumc.edu/gadepalli.3/BISR_RNASeq_ICIBM19 . Example dataset is demonstrated here: https://dataportal.bmi.osumc.edu/RNA_Seq/ . Conclusion The workflow allows for the analysis (alignment, QC, gene-wise counts generation) of raw RNAseq data and seamless integration of quality analysis and differential expression results into a configurable R shiny web application.
Type of Medium:
Online Resource
ISSN:
1471-2105
DOI:
10.1186/s12859-019-3251-1
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2019
detail.hit.zdb_id:
2041484-5
SSG:
12
